System and method for early identification of safety concerns of new drugs

ABSTRACT

A computer-implemented system and method for administering a drug registry database generates a display of a plurality of new drug/comparator drug pairings, wherein each new drug is added to the database when a threshold number of healthcare claims for the new drug have been received. Upon receiving a selection of a new drug/comparator drug pairing, and filter parameters input by a user, an analysis engine searches the healthcare claims stored in the database to identify a first and second group of patients satisfying the filter parameters input by the user; wherein the first group includes patients with claim data indicating use of the new drug and the second group includes patients with claim data indicating use of the comparator drug. The analysis engine further identifies a comparative occurrence of one or more existing medical conditions or new medical conditions in the first and second groups of patients and calculates a probability value indicating a relative likelihood that a patient taking the new drug will have each of the one or more new or existing medical conditions in comparison to a likelihood that a patient taking the comparator drug will have each of the one or more new or existing medical conditions. A user interface displays each new or existing medical condition and the probability value for the new drug and the comparator drug in accordance with one or more report parameters entered by the user.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.12/398,575, filed Mar. 5, 2009, now U.S. Pat. No. 7,966,196, which is acontinuation of U.S. patent application Ser. No. 11/377,628, filed Mar.16, 2006, now U.S. Pat. No. 7,917,374, which claims the benefit of U.S.Provisional Application No. 60/674,958, filed on Apr. 25, 2005. Thecontents of each of these applications are hereby incorporated byreference in their entirety.

FIELD OF THE INVENTION

This invention relates, in general, to data processing techniques, andmore specifically to analyzing healthcare data.

BACKGROUND OF THE INVENTION

During the last decade, the prescription drug approval period has beenshortened and the number of drugs receiving approval has risen. Whilemost of these drugs are safe, the emergence of information sufficient toprompt a safety recall often does not take place before widespread usageof the drug by a large patient population over many months or years. Inother words, some safety issues do not become apparent until after adrug has been taken by a large patient population over a period of time.

As recognized by the present inventors, what is needed is a method andsystem for early identification of safety concerns of new drugs.

SUMMARY

In light of the above and according to one broad aspect of oneembodiment of the present invention, disclosed herein is a method foridentifying safety concerns regarding a new drug. In one example, themethod utilizes a database of healthcare claims. The method may includeidentifying from the database a first group of patients that havereceived the drug; extracting one or more medical events that the firstgroup of patients have experienced; identifying a second group ofpatients that have received a comparator drug; extracting one or moremedical events that the second group of patients have experienced; andcomparing the one or more medical events of the first group to the oneor more medical events of the second group to determine one or morecommon occurrences therebetween.

Generally, the comparator drug is selected to have the same or similarpharmacological purpose as the new drug and the comparator drug willgenerally have been on the market for a greater period of time than thenew drug.

The method may also include computing a probability value for each ofthe one or more common occurrences. In this way, embodiments of thepresent invention may be used to identify and quantify the probabilitythat certain medical events will occur to patients taking the new drug.

The method may also employ various filters if desired, including forexample filtering the one or more common occurrences based on ages ofthe first and second group of patients; filtering the one or more commonoccurrences based on genders of the first and second group of patients;filtering the one or more common occurrences based on one or morediagnosis codes of the healthcare claims of the first and second groupof patients; filtering out pre-existing conditions from the one or morecommon occurrences of the healthcare claims of the first and secondgroup of patients.

In another example, the method may also include determining whether thedrug has been prescribed in an amount exceeding a minimum threshold,such as 1,000 patients for example.

According to another broad aspect of another embodiment of the presentinvention, disclosed herein is a system comprising a database ofhealthcare claims and an engine for identifying safety concernsregarding a new drug. The engine may be implemented as a computerprogram or process running on a computer or server. In one example, theengine includes a module for identifying from the database a first groupof patients that have received the drug; a module for extracting fromthe database one or more medical events that the first group of patientshave experienced; a module for identifying from the database a secondgroup of patients that have received a comparator drug; a module forextracting from the database one or more medical events that the secondgroup of patients have experienced; and a module for comparing the oneor more medical events of the first group to the one or more medicalevents of the second group to determine one or more common occurrencestherebetween.

Depending upon the implementation, the engine may perform variousfunctions, such as computing a probability value for each of the one ormore common occurrences; filtering the one or more common occurrencesbased on ages of the first and second group of patients; filtering theone or more common occurrences based on genders of the first and secondgroup of patients, and other operations or functions disclosed herein.

The features, utilities and advantages of the various embodiments of theinvention will be apparent from the following more particulardescription of embodiments of the invention as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a block diagram for identifying andanalyzing safety concerns regarding new drugs, in accordance with oneembodiment of the present invention.

FIG. 2 illustrates an example of operations for identifying safetyconcerns regarding new drugs, in accordance with one embodiment of thepresent invention.

FIG. 3 illustrates an example of a table that can be used to displayresults include the probability that a patient receiving the new drugwill experience a medical event or condition, in accordance with oneembodiment of the present invention.

FIG. 4 illustrates another example of a block diagram for identifyingand analyzing safety concerns regarding new drugs, in accordance withone embodiment of the present invention.

FIG. 5 illustrates an exemplary computer display screen providing alisting of drugs for selection as the new drug or as the comparator drugin accordance with one embodiment of the present invention.

FIG. 6 illustrates an exemplary computer display screen providing areport selection window in accordance with one embodiment of the presentinvention.

FIG. 7 illustrates an exemplary computer display screen providing atabular report view in accordance with one embodiment of the presentinvention.

FIG. 8 illustrates an exemplary computer display screen providingselectable filter parameters in accordance with one embodiment of thepresent invention.

FIGS. 9A and 9B illustrate an exemplary computer display screen of datamining processes in accordance with one embodiment of the presentinvention.

DETAILED DESCRIPTION

Embodiments of the present invention provide various methods (as well ascomputer implementations) for early identification of drug safetyconcerns based on medical claims data. Various embodiments of thepresent invention are described herein.

FIG. 1 illustrates a block diagram of one embodiment of the presentinvention. In one example, a conventional health care claims database isutilized which contains claims, including procedure code descriptions,such as a CPT code, for each claim line item. The claims data may alsoinclude ICD-9 codes, which are an International Classification ofDisease indicating why a particular procedure or medical service wasperformed, also known as a “diagnosis” codes.

An analysis engine is provided for analyzing data from the claimsdatabase in order to provide for early identification of safety concernsfor new drugs. A user interface may be provided to the analysis engineso that a user can perform analysis to identify potential safetyconcerns for specific new drugs, and generate reports, displays, etc.Further analysis may be conducted by the user in various manners, asdisclosed herein. The analysis engine may perform one or more of theoperations described herein for analyzing the data in the claimsdatabase for early identification of safety concerns for new drugs.

FIG. 2 illustrates an example of operations for early identification ofsafety concerns for new drugs, in accordance with one embodiment of thepresent invention. At operation 20, a new drug is identified as one thatwill be tracked using a claims database, for purposes of tracking users'experiences with the new drug. In one example, the database includesdata from healthcare claims, including IDC9 codes and pharmacologicalcodes. These codes are well known in the art and are associated withspecific diagnoses and treatments that patients receive from treatingphysicians as reported to medical insurance companies.

At operation 22, the number of patients prescribed the new drug ismonitored or checked to see if that number has exceeded a threshold. Inone example, operation 22 determines whether the new drug has beenprescribed to more than 1,000 patients. It is understood that differentthresholds can be utilized depending upon the implementation. Once thedrug has been prescribed to a number of patients exceeding thethreshold, then operation 22 passes control to operation 24.

At operation 24, a new drug patient group is identified from the claimsdatabase. In one example, the claims database is searched for allpatients that have been prescribed the new drug, and for each patientthat has been prescribed the new drug, all data relating to that patientin the claims database is extracted and stored in a data file or otherdata structure. This data may include the medical experience of each ofthe patients that have been prescribed the new drug, including the IDC9and CPT code data for those patients which may include events, symptoms,and other data relating to the patients and their medical experiences.

At operation 26, a comparator drug is identified. In a broad sense, acomparator drug may include a drug similar to or prescribed for asimilar purpose as the new drug. The comparator drug generally may becharacterized as being a more mature drug in the marketplace, forinstance, the comparator drug may have been FDA approved and prescribedby physicians in the ordinary course of treating patients. In oneexample, a comparator drug is selected manually based on similarcharacteristics between the comparator drug and the new drug. In anotherexample, a propensity score is generated to assist in the selection of acomparator drug.

At operation 28, having identified a comparator drug at operation 26, acomparator drug patient group is identified. Operation 28 may includeextracting claims data from the claims database for each person that hasbeen prescribed the comparator drug. Statistical analysis can beperformed upon this patient data to help refine the group of users ofthe comparator drug. Once the comparator drug patient group has beenselected, the experiences of the patients of the comparator drug patientgroup may be extracted from the claims database and stored in a file orother data structure.

At operation 30, the experiences of the new drug patient group and thecomparator drug patient group are compiled. Operation 30 compiles theseexperiences based on the IDC9 code data or other claims data gathered byoperations 24 and 28.

Operation 30 may compare the data from the new drug patient group to thecomparator drug patient group in order to determine, statistically, thefrequency of common occurrences between the patient groups. This datamay be expressed in a table or other representation in a number offorms. In one example, a table may be created (such as shown in FIG. 3),wherein a column represents the experiences of patients who use the newdrug, another column represents the experiences of patients who use thecomparator drug, and a third column may indicate the adjusted “p value”which indicates the probability that certain experiences may occur basedupon usage of the new drug.

At operation 32, the data for the new drug patient group and thecomparator drug patient group may be filtered in various manners. In oneexample, for each patient, the date at which the patient was prescribedeither the new drug (or the comparator drug) is identified, and thefiltering process may include identifying symptoms that the patient hadprior to the prescription of the new drug (or the comparator drug). Ineffect, this filtering operation identifies pre-existing conditionsprior to the prescription of the new drug (or the comparator drug) for aparticular patient. This pre-existing condition data can then befiltered out or excluded from the analysis, if desired.

At operation 34, the medical experiences of the patients after the dateof their prescriptions of the new drug (or comparator drug) can beanalyzed. For instance, in one example, the conditions that exist twelvemonths after a patient has been prescribed the new drug (or comparatordrug) may be compiled in order to determine whether such medicalconditions are common to users of the comparator drug and of the newdrug within the first twelve months after prescription. This treatmentemergent diagnosis helps identify and statistically analyze thecodes/medical conditions that occur after user of either the new orcomparator drug.

At operation 36, a probability “p value” is computed for each possiblemedical experience based on the data previously compiled and analyzed.Operation 36, in effect, calculates a “p value” for certain medicalevents or symptoms, and in this way, operation 36 can statisticallyquantify the probability that a patient taking the new drug mayexperience a particular medical event or symptom. Operation 36 mayutilize conventional data mining techniques for statistical calculationsin order to account for irrelevant or statistically insignificantoccurrences.

Upon computing the probability “p value” for statistically significantmedical event or symptom potentially associated with use of the newdrug, this data can be incorporated into a table or other form forreports or displays. FIG. 3 illustrates an example of a table that maybe created based upon one or more operations disclosed herein. It isunderstood that FIG. 3 is provided by way of example only, and is notintended to limit the scope of the present invention.

As shown in FIG. 3, the columns of the table include the medicalexperience type (such as conditions, symptoms or events or otherexperiences), and for the new drug patient group, a column indicatingthe number of occurrences of the particular experience along with thenumber of patients in the new drug group. Another column may be providedwhich shows, for the comparator drug patient group, the number ofoccurrences of the particular experience, along with the number ofpatients in the comparator drug patient group. For each particularexperience, a probability or “p value” may be calculated and displayed.Note that the values provided in this example table of FIG. 3 are notbased upon actual calculations, but rather are merely example fictitiousnumbers provided only for the purpose of illustrating how data may bearranged and displayed.

For instance, from the example table, it can be seen that the number ofoccurrences of high cholesterol for the new drug patient group was 37out of 1,000, and for the comparator drug patient group, the number ofoccurrences was 377 out of 25,672. A “p value” for high cholesterol isillustrated as 0.024 in this hypothetical example.

In accordance with one embodiment of the present invention (not shown),p-values may be used for internal calculations, while a simplernumerical value derived from the p-value is displayed by the system. Forexample, the following conversion may be used:

p-value range displayed score  >0.10 0 ≦0.10 and >0.01 1 ≦0.01and >0.001 2 ≦0.001 and >0.01 3

Additional values may be calculated using the same methodology. Also,the sign of the displayed score may be adjusted to be positive whenthere are more events for the new drug than for the comparator andnegative when there are fewer events for the new drug than for thecomparator.

Embodiments of the present invention may be implemented in a computingsystem or in a network based client server or ASP model. As shown inFIG. 4, a web-based interface can be provided so that users in variousremote locations can perform the analysis of the data to identify safetyconcerns for new drugs. For instance, using an internet based system,graphical user interfaces can be provided to the users which provide theusers (such as drug companies or governmental agencies) with the abilityto further analyze the results of the data. For instance, analysis canbe done which further analyzes or focuses on categories such as menolder than 65, men older than 65 that are also using another third drug,etc.

In another embodiment of the present invention, a drug registry iscreated and provided. In one example, a drug registry may be aninformation product that will provide early information on the healthcare experience of people taking new drugs in the United States or othercountries. The drug registry may use data including a robust crosssection of people, and can link patient and physician data, pharmacy andmedical claims data, and lab and diagnostic test results.

The drug registry can provide faster access to data to help researchersassess the relative risks of prescribed drugs, and in other embodiments,can provide access to integrated medical and prescription claimsinformation on prescription drugs introduced on the market for whichclaims for reimbursement have been submitted. Due to the volume ofexperiences that are reflected in the database, even a drug prescribedin moderate volumes will likely be reflected in enough data to performmeaningful analysis.

In one example, the drug registry can help those responsible for drugsafety make more timely and evidence-based decisions. In the lastdecade, more than 300 new prescription drugs were launched and theprescription drug approval period has been shortened. In addition,pharmaceutical and biotechnology companies are investing record amountsin research and development to bring more prescription drugs tomarket—in 2004, the industry invested $38.8 billion in R&D. But onceclinical trials are completed and a drug is on the market, the emergenceof information sufficient to prompt a safety recall often does not takeplace before widespread usage of the drug by a large patient populationover many months or years. Embodiments of the present invention can helporganize a large and growing source of health experience data in waysthat may enable faster detection of significant trends.

The drug registry may be created and used to provide faster access todata to help researchers assess the relative risks of a prescribed drug.The drug registry can be a resource that large health plans,pharmaceutical companies, regulatory agencies like the U.S. Food andDrug Administration (FDA), and other stakeholders can use to reviewhealth care data relating to prescription drug use in a timely andobjective manner. In addition, the results of the analysis of this datamay ultimately help physicians write prescriptions with greaterconfidence, and allow patients to take new medications with addedcomfort and security. Such data may help researchers detect potentiallysignificant trends earlier in a drug's market experience.

In one example, a drug registry may include a review of claims datagenerated by selected health plans. New molecular entities (“NMEs”—newlyintroduced drugs based on an innovative chemical structure) will enterthe Registry as soon as 1,000 individuals in a health plan have received(i.e. filled a prescription) the drug. A comparison group of users of anestablished therapy with the same indications will be created. Throughstatistical matching, the two groups, NME and comparator, will havenearly identical demographic characteristics and health insuranceexperience. In one example, the two groups will have, within statisticalprecision, the same distribution over categories of age, sex, geographicregion, diagnoses, drugs dispensed, procedures performed, andgeneralists and specialists seen. Within these groups, the drug registrymay track the occurrence of medical conditions—both existing and new.

Proper selection of the comparator reference group of persons notreceiving the NME will permit the drug registry to distinguish thedistinct effect (good or bad) of an NME on the occurrence ofcomplications that affect all drugs in a class, such as for example deepvein thrombosis in oral contraceptive users. The comparator group willalso filter out many new diagnoses that represent the naturalprogression of the disease being treated, such as visual disorders indiabetics.

In one example, two groups of users are identified—users of establishedtherapies, and users of newly introduced drugs—with both groupsfeaturing closely matched demographic characteristics (e.g., age, sex,geographic region, diagnoses, drugs dispensed, procedures performed,etc.). Within these groups, the drug registry can offer data that mayhelp researchers track the comparative occurrence of existing and newmedical conditions.

By using a comparator, better benchmarking of a new drug can be realizedwith a view toward its patient population and the particular healthissues typical of those patients. In addition, the comparator providesvalidation; it attempts to level the playing field to confirm that thesymptoms showing are in fact true and are not an idiosyncrasy due toupcoding or some other claims issue.

Focusing on new diagnoses will aid the drug registry in solving the“forest and trees” problem—how to separate out new drug effects from thewelter of ongoing conditions that were present before patients beganusing the NME.

Data mining—the use of sophisticated search algorithms to identifypatterns of newly emergent conditions that might otherwise have escapednotice—may also be employed.

For example, a data mining algorithm may be employed to systematicallyevaluate the frequency of treatment-emergent codes among the NME groupand the comparator group in subgroups of patients to detect ifpronounced imbalance of the occurrence of codes existed in specificsubgroups. As there is no a priori assumption about predefinedsubgroups, this process is referred to as data mining.

The data mining algorithm is described as follows:

Baseline attributes that are evaluated in data mining include: agegroup, sex, geographic region, diagnoses codes (3-digit level,inpatient, outpatient physician, or mental health professional, the samedefinition used in the baseline diagnosis table), procedure codes (AHRQgroup level), drugs (therapeutic class level).

Outcomes that are evaluated in data mining include: treatment emergentinpatient primary diagnosis (ICD-9 codes at 3-digit level), treatmentemergent outpatient visit diagnosis (ICD-9 codes at 3-digit level), andtreatment emergent therapeutic drug class. Procedures for first leveldata mining include the following calculations:

N_(d): number of subjects in the NME group in each cohort or subcohort

N_(c): number of subjects in the comparator group in each cohort orsubcohort

x_(d): number of subjects in the NME group who had thetreatment-emergent code

x_(c): number of subjects in the comparator group who had thetreatment-emergent codex _(d) +x _(c) =x(total number of emergent events)N _(c) +N _(d) =N(total number of matched NME and comparator druginitiators)Score Calculation:If x _(d) =x*N _(d) /N, then score=0If x _(d) <x*N _(d) /N, then score=log₁₀(cumulative hypergeometric(x_(d) ,N _(d) ,x,N))If x _(d) >x*N _(d) /N, then score=−log₁₀(cumulative hypergeometric(x−x_(d) ,N−N _(d) ,x,N))

(Scores May be Truncated to Integer Values for Presentation.)

Once the calculations and scores have been calculated as describedabove, the algorithm:

(1) calculates a score for each row for each of the outcome tables atthe crude levcalculates a score for each row for each of the outcometables at the crude level (i.e., the full NME cohort and comparatorcohort);

(2) identifies all rows in which the absolute value of the score was 3or more;

(3) restricts the two cohorts to those that have a certain baselineattribute, one at a time, for example, starting from age 0-9 through65+, then proceeding to men, women, northeast, Midwest, south, west,ICD-9 code 001 through 999, each E code, each V code, all procedurecodes, and all drug classes;

(4) skips subsets in which N_(d)≦3 or N_(c)≦3 or both;

(5) within a subset, evaluates each row in each outcome table, one at atime, such that rows satisfying the following criteria are kept:(x_(d)+x_(c))>3 and ((N_(d)+N_(c))−(x_(d)+x_(c)))>3;

(6) apply the following quadratic filter (e.g., in Oracle) to each row,keeping rows that fulfill this condition:POWER(ABS(x _(d)−(x _(d) +x _(c))*N _(d)/(N _(d) +N _(c)))−0.5,2)≧(4*(x_(d) +x _(c))*N _(d)/(N _(d) +N _(c))*(1−N _(d)/(N _(d) +N _(c))))

(7) for all eligible rows in each table, calculate the score asdescribed above;

(8) for each row, compare the score in the subset and the score in thefull cohort and identify the subset and the row for which the absolutevalue of the score in the subset is larger than the absolute value ofthe score in the full cohort;

(9) for the subset and row combinations identified in steps (2) and (8)above, output the combinations for which the absolute value of the scorewas 3 or more to the Data Mining Output tables (see FIGS. 9A and 9Bbelow).

In addition to data mining at the full cohort level, the algorithmdescribed above in steps (1) through (9) may be applied to a definedsubgroup. For example, a user may specify that Data Mining is to beapplied to all men. In this case, at step (3), all attributes other than“sex” will be evaluated. Similarly, for data mining in the subgroup “age40 or older,” at step (3) all attributes other than “age” will beevaluated.

Other types of data mining algorithms may also be implemented in thesystem and method according to the present invention.

Drug withdrawals and labeling changes commonly occur following years ofaccumulation of patient experience. Embodiments of the present inventioncan complement existing safety monitoring techniques, which now consistprimarily of doctor and patient reports and occasional dedicatedsurveillance programs mandated by the FDA. By greatly adding to the flowof relevant safety data, a drug registry in accordance with the presentinvention can support earlier identification of safety signals andearlier action.

Such a drug registry can offer researchers the data to analyzereal-world prescription drug 30 experience, including the healthexperiences of patients with co-morbidities, or those taking multiplemedications. Further, the registry can provide a much greater scope ofdata, which may allow researchers to identify more rare side effectsthat did not surface in prior analysis.

In one example, the drug registry can provide integrated medical andprescription claims information on drugs introduced since 2004 orearlier (or any prescription drug on the market can be studied), forwhich claims for reimbursement have been submitted. Due to the volume ofexperiences that are reflected in databases, even a drug prescribed inmoderate volumes will likely be reflected in enough data to performmeaningful analysis.

In one example, the resulting drug registry information will beavailable in two formats: quarterly, web-based files suitable formanipulation and analysis by informed users, as well as an annual reportof static data. Several product options, both online and print, may beprovided. These range from annual comprehensive publications, tosubscription services with rapid access to advanced analytics andquerying capabilities, to a hard copy annual report that could serve asa key desk-side resource to pharmacy and therapeutic committees andhospital formulary committees. An annual report of all drugs in theregistry can also be provided. Manufacturers may opt for more detailedinformation about how their drug is being used in the marketplace.

The registry can benefit a wide range of industry stakeholders. Theregistry will benefit a wide range of industry stakeholders, includingpharmaceutical and biotech companies, investment firms, managed careorganizations, government entities, hospitals, advocacy groups, andlarge employers In certain embodiments, the drug registry may:

Allow researchers to quantify already-known side effects to helpphysicians make therapeutic decisions about which drugs may be mostappropriate for various patients

Provide pharmaceutical manufacturers with a resource that can delivervalue from R&D to commercialization Assist managed care organizationsand large employers 25 with creation and updating of drug formularies

Assist the government with regulatory and compliance decision-making

Support prompt detection of critical safety issues, helping physiciansand patients feel greater comfort and confidence in new medications

The information in the drug registry can be made to be compliant withrelevant HIPAA regulations.

One or more features of the present invention may be implementedutilizing various graphical user interfaces with various controls anddisplays of information to a user. FIGS. 5-9 illustrate example displayscreens, and one or more of these example display screens or portionsthereof may be utilized when implementing embodiments of the presentinvention.

Generally, the graphical user interfaces may be arranged so as toprovide the user with the ability to select a new drug and a comparatordrug from a drug list; and to generate various reports by controlling orviewing various characteristics. For instance, reports may be viewed intabular form and may include a variety of different standard reports,filtered views where the user may define characteristics of the views,and data mining of the filtered views may also be provided, Variousfilter parameters may be specified, in one example, such as filtering bythe drug, age, gender, diagnosis code, and days since first dispensingthe particular drug. In one example, filters established by the user maybe saved for later use or modification.

In FIG. 5, the drug to be examined as well as the comparator drugs canbe selected by the user. For instance, a list of available drugs for useas the comparator drug can be provided in one example.

Upon having selected the new drug and the comparator drug, a user maythen select the type of report the user is interested in generating.FIG. 6 illustrates an example display screen wherein a plurality ofstandard report types are illustrated. If desired, the user may select areport showing, for the drug/comparator drug, the demographiccharacteristics, prevalence of diagnosis, prevalence of procedures,prevalence of drug class, healthcare utilization, characteristics of theprescribing parties, treatments that the drug has been used for, medicalconditions among the drug, procedures among the drug, drug classdispensing, health care utilization among the drug, and clusteredoutcome for the drug.

Further, if desired, the user may be provided with the ability to createfiltered views or data mining of an outcome from a standard report, suchas where other parameters may be filtered from or focused in upon usingthe results of a standard report (see FIG. 8 below).

FIG. 7 illustrates an example display screen of a tabular report viewshowing an example of how demographic characteristics of a drug orcomparator drug can be displayed. For the new drug under examination,the number of data points is displayed and the demographics of the usesof the drug based on healthcare claims data may be displayed, includingthe age of the patients, gender, and geographic region. A similardisplay may be utilized for the comparator drug.

Patient data may specifically be made available so that the user mayview the manner in which the test data or the comparator drug wasactually utilized in medical treatment.

FIG. 8 illustrates an example display screen relating to filterparameters. Various filter parameters may be provided so that a user canapply a filter to the results of a standard report. For instance, in oneexample, filter parameters may include the drug type, patient age,patient gender, diagnosis code, and the number of days since firstdispensing the particular drug. An “apply” but may be utilized so thatonce the user selects the particular filter, the filter may be appliedto the results previously generated by the standard report. In this way,the user may, if desired, filter out certain parameters in order tobetter isolate the data of interest.

For instance, if the user is interested in the effect of a particulardrug on infants six months old or younger, the user could utilize theage filter to filter out any patient data relating to patients whose ageis greater than six months old. The filters which are created by theuser and their resultants report data may be saved in a file for laterreference.

FIGS. 9A and 9B illustrate an exemplary display screen relating to datamining. FIG. 9B illustrates the same chart as FIG. 9A, but the displayin FIG. 9B is shifted to the right to show the final column of the chartillustrated in FIGS. 9A and 9B. The values shown in FIGS. 9A and 9B areas follows:

X_(d)=number of emergent events among matched NME initiators

N_(d)=number of matched NME initiators

x_(c)=number of emergent events among matched comparator drug initiators

N_(c)=number of matched comparator drug initiatorsX _(d) x _(c) =x(total number of emergent events)N _(c) +N _(d) =N(total number of matched NME and comparator druginitiators)

Calculations are performed as follows:RR=(xd/Nd)/(xc/Nc)95% Confidence Interval (CI):Lower Boundary:Pd=(x _(d)-0.5)/xD=(1.96)² /xA=1+DB=−2*P _(d) −DC=(P _(d))2P _(lower)=(−B−SQRT(B ²−4*A*C))/(2*A)RR _(lower)=(P _(lower) *x/N _(d))/((x−P _(lower) *x)/N _(c))However, if RR=0, then RR_(lower)=0Upper Boundary:Pd=(x _(d)−0.5)/xD=(1.96)² /xA=1+DB=−2*P _(d) −DC=(P _(d))²P _(upper)=(−B+SQRT(B ²−4*A*C))/(2*A)RR _(upper)=(P _(upper) *x/N _(d))/((x−P _(upper) *x)/N _(c))However, if RR=infinity, then RR_(upper)=infinityScore:If x _(d) =x*N _(d) /N, then score=0If X _(d) <X*N _(d) /N, then score=log₁₀(cumulative hypergeometric(x_(d) ,N _(d) ,x,N))If X _(d) >x*N _(d) /N, then score=−log₁₀(cumulative hypergeometric(x−x_(d) ,N−N _(d) ,x,N))

In FIGS. 9A and 9B, scores are truncated to integer values forpresentation.

Hence, it can be seen that embodiments of the present invention providevarious methods (as well as computer implementations) for earlyidentification of drug safety concerns.

Embodiments of the invention can be embodied in a computer programproduct. It will be understood that a computer program product includingfeatures of the present invention may be created in a computer usablemedium (such as a CD-ROM or other medium) having computer readable codeembodied therein. The computer usable medium preferably contains anumber of computer readable program code devices configured to cause acomputer to affect the various functions required to carry out theinvention, as herein described.

While the methods disclosed herein have been described and shown withreference to particular operations performed in a particular order, itwill be understood that these operations may be combined, sub-divided,or re-ordered to form equivalent methods without departing from theteachings of the present invention. Accordingly, unless specificallyindicated herein, the order and grouping of the operations is not alimitation of the present invention.

It should be appreciated that reference throughout this specification to“one embodiment” or “an embodiment” or “one example” or “an example”means that a particular feature, structure or characteristic describedin connection with the embodiment may be included, if desired, in atleast one embodiment of the present invention. Therefore, it should beappreciated that two or more references to “an embodiment” or “oneembodiment” or “an alternative embodiment” or “one example” or “anexample” in various portions of this specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures or characteristics may be combined as desired inone or more embodiments of the invention.

It should be appreciated that in the foregoing description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosure andaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed inventions require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment, and each embodimentdescribed herein may contain more than one inventive feature.

While the invention has been particularly shown and described withreference to embodiments thereof, it will be understood by those skilledin the art that various other changes in the form and details may bemade without departing from the spirit and scope of the invention.

1. A computer-implemented system for administering a drug registrydatabase, comprising: an electronic database containing at least aplurality of healthcare claims of a plurality of patients and aplurality of new drug/comparator drug pairings; an electronic userinterface configured to display the plurality of new drug/comparatordrug pairings, wherein each new drug is added to the database when athreshold number of healthcare claims for the new drug have beenreceived and wherein each comparator drug paired with a new drug thathas substantially similar medical indications as the new drug; andreceive a selection of the new drug/comparator drug pairing, and filterparameters input by a user; and a computer-implemented analysis engineconfigured to search the healthcare claims stored in the database toidentify a first and second group of patients satisfying the filterparameters input by the user, wherein the first group includes patientswith claim data indicating use of the new drug and the second groupincludes patients with claim data indicating use of the comparator drug;identify a comparative occurrence of one or more existing medicalconditions or new medical conditions in the first and second groups ofpatients; and calculate a probability value indicating a relativelikelihood that a patient taking the new drug will have each of the oneor more new or existing medical conditions in comparison to a likelihoodthat a patient taking the comparator drug will have each of the one ormore new or existing medical conditions; wherein the electronic userinterface displays each new or existing medical condition and theprobability value for the new drug and the comparator drug in accordancewith one or more report parameters entered by the user.
 2. The systemaccording to claim 1, wherein the display generated by the electronicuser interface includes demographic characteristics, prevalence ofdiagnosis, prevalence of procedures, prevalence of drug class, orhealthcare utilization.
 3. The system according to claim 1, wherein thefilter parameters include one or more diagnosis codes, procedure codes,drug categories, or demographic characteristics.
 4. Acomputer-implemented method for administering a drug registry database,comprising: storing a plurality of healthcare claims for a plurality ofpatients on a computing device and a plurality of new drug/comparatordrug pairings; displaying on an electronic user interface a plurality ofnew drug/comparator drug pairings, wherein each new drug is added to thedatabase when a threshold number of healthcare claims for the new drughave been received and wherein each comparator drug paired with a newdrug that has substantially similar medical indications as the new drug;receiving a selection of the new drug/comparator drug pairing, andfilter parameters input by a user; searching by a computer-implementedanalysis engine the healthcare claims stored in the database to identifya first and second group of patients satisfying the filter parametersinput by the user; wherein the first group includes patients with claimdata indicating use of the new drug and the second group includespatients with claim data indicating use of the comparator drug;identifying a comparative occurrence of one or more existing medicalconditions or new medical conditions in the first and second groups ofpatients; calculating a probability value indicating a relativelikelihood that a patient taking the new drug will have each of the oneor more new or existing medical conditions in comparison to a likelihoodthat a patient taking the comparator drug will have each of the one ormore new or existing medical conditions; and displaying each new orexisting medical condition and the probability value for the new drugand the comparator drug in accordance with one or more report parametersentered by the user.
 5. The method according to claim 4, wherein thedisplay generated by the electronic user interface includes demographiccharacteristics, prevalence of diagnosis, prevalence of procedures,prevalence of drug class, or healthcare utilization.
 6. The methodaccording to claim 4, wherein the filter parameters include one or morediagnosis codes, procedure codes, drug categories, or demographiccharacteristics.